Representing a function of one variable in terms of a sequence of gaussians

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SUMMARY

This discussion centers on representing a function of one variable using a sequence of Gaussian functions. It highlights the potential of radial basis functions for interpolation, specifically through the formula σ(x) = ∑(j=1 to N) a_j e^{-(x-x_j)²/c²}, where c is a constant and {x_j} are nodes. The conversation emphasizes that while Gaussian functions can be summed to approximate a function that decays to zero at infinity, this method provides interpolation rather than an exact representation. The discussion also touches on the relevance of wavelets in this context.

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  • Understanding of Gaussian functions and their properties
  • Familiarity with radial basis functions and their applications
  • Knowledge of interpolation techniques in numerical analysis
  • Basic concepts of wavelets and their mathematical foundations
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thoughtgaze
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Is this possible? It seems like it should be but, it's difficult to find an explicit relationship between a general function of one variable x (let's say we are only interested in functions that decay to zero as they go to plus or minus infinity)

it seems like summing a bunch of gaussians of arbitrary width located at various points along the domain, one should be able to construct an arbitrary function but I don't see much useful material on it.
 
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Look at the topic of "radial basis functions" and think of the 1-dimensional case.

You also might find the topic of "wavelets" interesting. I don't know whether one can make useful "wavelets" with a gaussian shape.
 
Radial basis functions are the answer if by "representing" you mean "interpolating". Let [itex]f[/itex] be a function and consider the interpolant
[tex]\sigma(x) = \sum_{j=1}^N a_j e^{-\frac{(x-x_j)^2}{c^2} },[/tex]
for some [itex]c[/itex] and some set of nodes [itex]\{x_j\}[/itex]. The interpolation conditions [itex]f(x_j) = \sigma(x_j)[/itex] give you a system of equations for the coefficients [itex]a_j[/itex]. This system always has a solution, because the gaussian is positive definite. However, note that this is not an exact representation, you are just interpolating.
 

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